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This paper describes a method of lane map building and localization for automated driving using 2d laser rangefinder. Today's on-board sensors such as radar or camera do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support these systems. An digital map is used as a powerful additional sensor. So we propose a lane map-based localization using a 2D Laser Rangefinder. The maps are created beforehand using a 2D LiDAR and RTK GPS. A pose estimation of vehicle was derived from a low-cost GPS and an iterative closest point(ICP) match of real-time sensor data to lane map. And the estimated pose was used as an observation inside a Kalman filter framework. The performance of the proposed localization algorithm is verified via vehicle tests in ITS proving ground. It has been shown through vehicle tests that good localization performance can be obtained. The proposed algorithm will be useful in the implementation of automated driving.
Kim et al. (Mon,) studied this question.